Fusion-based Process Discovery
Modern information systems record the execution of transactions as part of business processes in event logs. Process mining is a rapidly growing research ﬁeld that aims at the analysis of business processes based on such event logs. Speciﬁcally, various algorithms for the discovery of process models from event logs have been developed in recent years. Each of these algorithms with speciﬁc advantages and limitations.In the talk, we argue that the results of process discovery can be improved by combining several of the existing algorithms. Instead of relying on a single algorithm, the outcomes of different discovery algorithms are fused to combine the strengths of individual approaches.The contribution comprises a general framework for such fusion and its instantiation with two new discovery algorithms: The exhaustive noise-aware inductive miner (exNoise), which, exhaustively searches for model improvements; and the adaptive noise-aware inductive miner (adaNoise), which is a computationally tractable version of exNoise. For both algorithms, we formally show that they outperform each of the individual mining algorithms used by them. Our empirical evaluation further illustrates that fusion-based discovery yields models of better quality than state-of-the-art approaches.